Inactive enzyme homologs, or pseudoenzymes, are proteins, found within most enzyme families, that are incapable of performing catalysis. Rather than catalysis, they are involved in protein-protein interactions, sometimes regulating the activity of their active enzyme cousins, or scaffolding protein complexes. Pseudoenzymes found within metallopeptidase families likewise perform these functions. Pseudoenzymes within the M14 carboxypeptidase family interact with collagens within the extracellular space, while pseudopeptidase members of the M12 \"a disintegrin and metalloprotease\" (ADAM) family either discard their pseudopeptidase domains as unnecessary for their roles in sperm maturation or utilize surface loops to enable assembly of key complexes at neuronal synapses. Other metallopeptidase families contain pseudopeptidases involved in protein synthesis at the ribosome and protein import into organelles, sometimes using their pseudo-active sites for these interactions. Although the functions of these pseudopeptidases have been challenging to study, ongoing work is teasing out the secret lives of these proteins.

Injection molding technology is widely utilized across various industries for its ability to fabricate complex-shaped components with exceptional dimensional accuracy. However, challenges related to injection quality often arise, necessitating innovative approaches for improvement. This study investigates the influence of surface roughness on the efficiency of conformal cooling channels produced using additive manufacturing technologies, specifically Direct Metal Laser Sintering (DMLS) and Atomic Diffusion Additive Manufacturing (ADAM). Through a combination of experimental measurements, including surface roughness analysis, scanning electron microscopy, and cooling system flow analysis, this study elucidates the impact of surface roughness on coolant flow dynamics and pressure distribution within the cooling channels. The results reveal significant differences in surface roughness between DMLS and ADAM technologies, with corresponding effects on coolant flow behavior. Following that fact, this study shows that when cooling channels\' surface roughness is lowered up to 90%, the reduction in coolant media pressure is lowered by 0.033 MPa. Regression models are developed to quantitatively describe the relationship between surface roughness and key parameters, such as coolant pressure, Reynolds number, and flow velocity. Practical implications for the optimization of injection molding cooling systems are discussed, highlighting the importance of informed decision making in technology selection and post-processing techniques. Overall, this research contributes to a deeper understanding of the role of surface roughness in injection molding processes and provides valuable insights for enhancing cooling system efficiency and product quality.

Unsupervised learning based on restricted Boltzmann machine or autoencoders has become an important research domain in the area of neural networks. In this paper mathematical expressions to adaptive learning step calculation for RBM with ReLU transfer function are proposed. As a result, we can automatically estimate the step size that minimizes the loss function of the neural network and correspondingly update the learning step in every iteration. We give a theoretical justification for the proposed adaptive learning rate approach, which is based on the steepest descent method. The proposed technique for adaptive learning rate estimation is compared with the existing constant step and Adam methods in terms of generalization ability and loss function. We demonstrate that the proposed approach provides better performance.

OBJECTIVE: To describe clinical characteristics, hormonal profile and body composition of obese men in preoperative of bariatric surgery.
METHODS: Cross-sectional, population-based study. Patients evaluated from June 2019 to December 2021 in 2 obesity referral centers. Patients underwent clinical evaluation, androgen deficiency screening using Androgen Deficiency in the Aging Male questionnaire, hormonal profile and body composition assessment through body mass index (BMI), body fat percentage (FM-%) and mass (FM-kg) measured by electrical bioimpedance and dual energy x-ray absorptiometry. To characterize hypogonadism, 2 cut-off points were considered: TT <264 ng/dL and TT <164 ng/dL.
RESULTS: Thirty patients were included, mean age 35.6 ± 8.8 years, mean weight 129.4 ± 14.0 kg and mean BMI 42.3 ± 4.7 kg/m2. Dyslipidemia was the most prevalent comorbidity. Considering TT <264 ng/dL, 22 patients (73%) had hypogonadism. The mean TT in hypogonadal men was 198.9 + 68.7 ng/dL and in eugonadal men 357.0 + 59.5 ng/dl (P < .001). Using TT <164 ng/dL, 7 patients (23%) had hypogonadism. The mean TT in hypogonadal patients was 116.6 + 28.9 ng/dL and in eugonadal patients 279.0 + 75.0 ng/dL (P < .001). In Androgen Deficiency in the Aging Male questionnaire, 93.3% had positive screening, with no significant difference between groups. There was no statistically significant difference in body composition between groups when using TT <264 ng/dL as the hypogonadism cutoff. Considering hypogonadism TT <164 ng/dL, hypogonadal patients had significantly higher values of weight (139.0 × 126.5 kg P = .036), BMI (46.1 × 41.2 kg/m2P = .014), FM-% (48.0 × 42.8% P = .010) and FM-kg (66.3 × 53.9 kg P = .007) than eugonadal patients.
CONCLUSIONS: Hypogonadism was identified in at least 23% of patients. Considering TT below the lower limit of normality for characterization of hypogonadism, we identified a significant worsening in body composition parameters.

Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sector by providing promising results in diagnosing, detecting, and classifying diseases. This paper explores the role of deep learning techniques in detecting and classifying various parasitic organisms. The research works on a dataset consisting of 34,298 samples of parasites such as Toxoplasma Gondii, Trypanosome, Plasmodium, Leishmania, Babesia, and Trichomonad along with host cells like red blood cells and white blood cells. These images are initially converted from RGB to grayscale followed by the computation of morphological features such as perimeter, height, area, and width. Later, Otsu thresholding and watershed techniques are applied to differentiate foreground from background and create markers on the images for the identification of regions of interest. Deep transfer learning models such as VGG19, InceptionV3, ResNet50V2, ResNet152V2, EfficientNetB3, EfficientNetB0, MobileNetV2, Xception, DenseNet169, and a hybrid model, InceptionResNetV2, are employed. The parameters of these models are fine-tuned using three optimizers: SGD, RMSprop, and Adam. Experimental results reveal that when RMSprop is applied, VGG19, InceptionV3, and EfficientNetB0 achieve the highest accuracy of 99.1% with a loss of 0.09. Similarly, using the SGD optimizer, InceptionV3 performs exceptionally well, achieving the highest accuracy of 99.91% with a loss of 0.98. Finally, applying the Adam optimizer, InceptionResNetV2 excels, achieving the highest accuracy of 99.96% with a loss of 0.13, outperforming other optimizers. The findings of this research signify that using deep learning models coupled with image processing methods generates a highly accurate and efficient way to detect and classify parasitic organisms.

BACKGROUND: Diabetic Retinopathy (DR) is a growing problem in Asian countries. DR accounts for 5% to 7% of all blindness in the entire area. In India, the record of DR-affected patients will reach around 79.4 million by 2030.
OBJECTIVE: The main objective of the investigation is to utilize 2-D colored fundus retina scans to determine if an individual possesses DR or not. In this regard, Engineering-based techniques such as deep learning and neural networks play a methodical role in fighting against this fatal disease.
METHODS: In this research work, a Computational Model for detecting DR using Convolutional Neural Network (DRCNN) is proposed. This method contrasts the fundus retina scans of the DR-afflicted eye with the usual human eyes. Using CNN and layers like Conv2D, Pooling, Dense, Flatten, and Dropout, the model aids in comprehending the scan\'s curve and color-based features. For training and error reduction, the Visual Geometry Group (VGG-16) model and Adaptive Moment Estimation Optimizer are utilized.
RESULTS: The variations in a dataset like 50%, 60%, 70%, 80%, and 90% images are reserved for the training phase, and the rest images are reserved for the testing phase. In the proposed model, the VGG-16 model comprises 138M parameters. The accuracy is achieved maximum rate of 90% when the training dataset is reserved at 80%. The model was validated using other datasets.
CONCLUSIONS: The suggested contribution to research determines conclusively whether the provided OCT scan utilizes an effective method for detecting DRaffected individuals within just a few moments.

Silencing expression with short interfering RNA (siRNA) is a rapid and cost-effective way to analyze the involvement of target genes in a range of biological processes. Here we describe isolation of primary human monocytes from peripheral blood and their in vitro differentiation to macrophages, followed by electroporation with siRNA to silence expression of a disintegrin and metalloproteinase 17 (ADAM17). This enables evaluation of ADAM17\'s role in cleaving transmembrane proteins, such as its prototypic substrate tumor necrosis factor (TNF), by enzyme-linked immunosorbent assay (ELISA), flow cytometry, or immunoblotting.

Microglia are resident innate immune cells in the central nervous system (CNS) and play important roles in the development of CNS homeostasis. Excessive activation and neurotoxicity of microglia are observed in several CNS disorders, but the mechanisms regulating their activation remain unclear. Immune checkpoint molecules are expressed on activated immune cells and regulate their activation in peripheral immunity. However, the expression mechanism of immune checkpoint molecules in activated microglia is still unknown. Here, we analyzed the expression of immune checkpoint molecules in activated microglia using the mouse microglial cell line BV2 and primary cultured microglia. The expression of lymphocyte activation gene-3 (LAG-3), a type of immune checkpoint molecule, was increased in microglia activated by IFN-γ. IFN-γ-induced LAG-3 expression in microglia was suppressed by transfection of siRNA targeting STAT1. LAG-3 has two forms, membrane and soluble, and both forms were upregulated in microglia activated by IFN-γ. The production of soluble LAG-3 was suppressed by treatment with inhibitors of metalloproteinases such as ADAM10 and ADAM17. IFN-γ administration into cisterna magna of mice increased LAG-3 expression in spinal microglia. Furthermore, LAG-3 knockdown in microglia promoted nitric oxide production by IFN-γ. Our results demonstrate that LAG-3 expression in microglia is induced by the IFN-γ-STAT1 pathway and soluble LAG-3 production is regulated via cleavage of membranous LAG-3 by metalloproteinases including ADAM10 and ADAM17.

Snake venom disintegrins are low molecular weight, non-enzymatic proteins rich in cysteine, present in the venom of snakes from the families Viperidae, Crotalidae, Atractaspididae, Elapidae, and Colubridae. This family of proteins originated in venom through the proteolytic processing of metalloproteinases (SVMPs), which, in turn, evolved from a gene encoding an A Disintegrin And Metalloprotease (ADAM) molecule. Disintegrins have a recognition motif for integrins in their structure, allowing interaction with these transmembrane adhesion receptors and preventing their binding to proteins in the extracellular matrix and other cells. This interaction gives disintegrins their wide range of biological functions, including inhibition of platelet aggregation and antitumor activity. As a result, many studies have been conducted in an attempt to use these natural compounds as a basis for developing therapies for the treatment of various diseases. Furthermore, the FDA has approved Tirofiban and Eptifibatide as antiplatelet compounds, and they are synthesized from the structure of echistatin and barbourin, respectively. In this review, we discuss some of the main functional and structural characteristics of this class of proteins and their potential for therapeutic use.

Introduction: Cranial neural crest (CNC) cells are induced at the border of the neural plate by a combination of FGF, Wnt, and BMP4 signaling. CNC then migrate ventrally and invade ventral structures where they contribute to craniofacial development. Methods: We used loss and gain of function experiments to determine phenotypes associated with the perturbation of Adam11 expression in Xenopus Laevis. Mass spectrometry to identify partners of Adam11 and changes in protein expression in CNC lacking Adam11. We used mouse B16 melanoma to test the function of Adam11 in cancer cells, and published database analysis to study the expression of ADAM11 in human tumors. Results: Here we show that a non-proteolytic ADAM, Adam11, originally identified as a putative tumor suppressor binds to proteins of the Wnt and BMP4 signaling pathway. Mechanistic studies concerning these non-proteolytic ADAM lack almost entirely. We show that Adam11 positively regulates BMP4 signaling while negatively regulating β-catenin activity. In vivo, we show that Adam11 influences the timing of neural tube closure and the proliferation and migration of CNC. Using both human tumor data and mouse B16 melanoma cells, we further show that ADAM11 levels similarly correlate with Wnt or BMP4 activation levels. Discussion: We propose that ADAM11 preserves naïve cells by maintaining low Sox3 and Snail/Slug levels through stimulation of BMP4 and repression of Wnt signaling, while loss of ADAM11 results in increased Wnt signaling, increased proliferation and early epithelium to mesenchyme transition.